{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fINen2x04dtG"
   },
   "outputs": [],
   "source": [
    "#cuda enabled gpu runtime\n",
    "\n",
    "!pip install ppscore\n",
    "!pip install catboost\n",
    "!pip install --upgrade pandas\n",
    "!pip install --upgrade numpy\n",
    "!pip install -U scikit-learn==0.21\n",
    "!pip install scikit-optimize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "ZXEHlFfs4202",
    "outputId": "5209e45f-4375-496f-96f3-f6ce385b09c8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /GD; to attempt to forcibly remount, call drive.mount(\"/GD\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import ppscore as pps\n",
    "from mlxtend.feature_selection import ColumnSelector\n",
    "from imblearn.over_sampling import BorderlineSMOTE\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from time import time\n",
    "from IPython.display import clear_output\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "# from reg_resampler import resampler\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.model_selection import KFold, cross_val_score,StratifiedKFold\n",
    "from imblearn.over_sampling import SMOTE \n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import KFold, train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder, LabelEncoder, label_binarize\n",
    "import csv\n",
    "import os\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "from catboost import CatBoostClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "import warnings\n",
    "# from fastai.structured import add_datepart\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.metrics import log_loss\n",
    "from keras.models import Sequential\n",
    "from google.colab import drive\n",
    "from sklearn import preprocessing\n",
    "from mlxtend.classifier import EnsembleVoteClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "import sklearn as sk\n",
    "from sklearn.metrics import confusion_matrix \n",
    "from matplotlib import pyplot\n",
    "from sklearn.cluster import KMeans\n",
    "from fancyimpute import KNN\n",
    "from catboost import CatBoostClassifier, Pool, cv\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "drive.mount(\"/GD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yAnxIahut--Y"
   },
   "outputs": [],
   "source": [
    "def split_numbers_chars(row):\n",
    "    head = row.rstrip('0123456789')\n",
    "    tail = row[len(head):]\n",
    "    return head, tail\n",
    "\n",
    "#tree models tend to perform well with categories as label encodes or similar instead of one hot encodes as that results in growing of trees in a singular direction\n",
    "def reverse_one_hot_encode(dataframe, start_loc, end_loc, numeric_column_name):\n",
    "    dataframe['String_Column'] = (dataframe.iloc[:, start_loc:end_loc] == 1).idxmax(1)\n",
    "    dataframe['Tuple_Column'] = dataframe['String_Column'].apply(split_numbers_chars)\n",
    "    dataframe[numeric_column_name] = dataframe['Tuple_Column'].apply(lambda x: x[1]).astype('int64')\n",
    "    dataframe.drop(columns=['String_Column','Tuple_Column'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8ii0oJmvsX9M"
   },
   "outputs": [],
   "source": [
    "\n",
    "df_train=pd.read_csv(\"/GD/My Drive/forest/train.csv\")\n",
    "df_test=pd.read_csv(\"/GD/My Drive/forest/test.csv\")\n",
    "\n",
    "reverse_one_hot_encode(df_train,14,54,'soil_type')\n",
    "reverse_one_hot_encode(df_train,10,14,'wilderness')\n",
    "reverse_one_hot_encode(df_test,14,54,'soil_type')\n",
    "reverse_one_hot_encode(df_test,10,14,'wilderness')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "8nK9TCdQs6jV",
    "outputId": "0c68c57b-6bbe-40cb-b669-af3b210f6ae7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Tree based relationships\n",
    "df=pps.matrix(df_train.drop(['Soil_Type_1', 'Soil_Type_2', 'Soil_Type_3', 'Soil_Type_4',\n",
    "       'Soil_Type_5', 'Soil_Type_6', 'Soil_Type_7', 'Soil_Type_8',\n",
    "       'Soil_Type_9', 'Soil_Type_10', 'Soil_Type_11', 'Soil_Type_12',\n",
    "       'Soil_Type_13', 'Soil_Type_14', 'Soil_Type_15', 'Soil_Type_16',\n",
    "       'Soil_Type_17', 'Soil_Type_18', 'Soil_Type_19', 'Soil_Type_20',\n",
    "       'Soil_Type_21', 'Soil_Type_22', 'Soil_Type_23', 'Soil_Type_24',\n",
    "       'Soil_Type_25', 'Soil_Type_26', 'Soil_Type_27', 'Soil_Type_28',\n",
    "       'Soil_Type_29', 'Soil_Type_30', 'Soil_Type_31', 'Soil_Type_32',\n",
    "       'Soil_Type_33', 'Soil_Type_34', 'Soil_Type_35', 'Soil_Type_36',\n",
    "       'Soil_Type_37', 'Soil_Type_38', 'Soil_Type_39', 'Soil_Type_40'],axis=1))\n",
    "fig, ax = plt.subplots(figsize=(20,20)) \n",
    "# sns.set(font_scale=1.3)\n",
    "g=sns.heatmap(df, vmin=0, vmax=1, cmap=\"Reds\", linewidths=3, annot=True,ax=ax,annot_kws={\"fontsize\":10})\n",
    "\n",
    "#wilderness and soile can be thus used for catboost removing there one hot encoded transformations\n",
    "#elevation seem to be a single important feature effecting target, thus we should develop some more features using it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "A-f-t9c1tixx",
    "outputId": "170c51e4-1b40-4332-b1a3-087e75cab53c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#identifying linear relationships and multicollinearity\n",
    "df=df_train.drop(['Soil_Type_1', 'Soil_Type_2', 'Soil_Type_3', 'Soil_Type_4',\n",
    "       'Soil_Type_5', 'Soil_Type_6', 'Soil_Type_7', 'Soil_Type_8',\n",
    "       'Soil_Type_9', 'Soil_Type_10', 'Soil_Type_11', 'Soil_Type_12',\n",
    "       'Soil_Type_13', 'Soil_Type_14', 'Soil_Type_15', 'Soil_Type_16',\n",
    "       'Soil_Type_17', 'Soil_Type_18', 'Soil_Type_19', 'Soil_Type_20',\n",
    "       'Soil_Type_21', 'Soil_Type_22', 'Soil_Type_23', 'Soil_Type_24',\n",
    "       'Soil_Type_25', 'Soil_Type_26', 'Soil_Type_27', 'Soil_Type_28',\n",
    "       'Soil_Type_29', 'Soil_Type_30', 'Soil_Type_31', 'Soil_Type_32',\n",
    "       'Soil_Type_33', 'Soil_Type_34', 'Soil_Type_35', 'Soil_Type_36',\n",
    "       'Soil_Type_37', 'Soil_Type_38', 'Soil_Type_39', 'Soil_Type_40'],axis=1)\n",
    "corr=df.corr()\n",
    "mask = np.triu(np.ones_like(corr, dtype=np.bool))\n",
    "plt.figure(figsize=(15,15))\n",
    "sns.heatmap(corr,vmin=-1,cmap='coolwarm',annot=True,mask=mask);\n",
    "#no strong linear relationships found with target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "colab_type": "code",
    "id": "EuXRwBkZw-IQ",
    "outputId": "7dff7ffb-dc3a-4491-e9e8-7ac2dc1296f6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f65731a8128>"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train.boxplot(column='Elevation(meters)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 318
    },
    "colab_type": "code",
    "id": "U4w9gkJ_4q50",
    "outputId": "64987b32-992b-4daa-dba2-980842e5240e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6572cbb518>"
      ]
     },
     "execution_count": 37,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train.boxplot(column='Elevation(meters)',by='wilderness')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "colab_type": "code",
    "id": "_ppf5ZjjxAp0",
    "outputId": "d6505ea1-22c8-4df7-9eba-0a38a5674031"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6572d7cbe0>"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_test.boxplot(column='Elevation(meters)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 318
    },
    "colab_type": "code",
    "id": "i89QxZdP4xJg",
    "outputId": "dd21a985-857f-4a49-fac2-c2b04ea039be"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f657972da90>"
      ]
     },
     "execution_count": 38,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_test.boxplot(column='Elevation(meters)',by='wilderness')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OpvjiblL4PRz"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 315
    },
    "colab_type": "code",
    "id": "MMIYtg1AxGcL",
    "outputId": "67498f51-6f82-4f09-973b-ba4f8d10c6b5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f657309d9b0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train.hist(column='soil_type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 315
    },
    "colab_type": "code",
    "id": "noG82AKmxRjK",
    "outputId": "60804804-9599-4d7b-dd94-012ed100e2b1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f6579856588>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m30B2Ivq5iNgHHJU0Nx1eugF4MA3bACxM0wuHxW9IVz/NBY6k+ZiZWR2NuEch6V6gHThL0gvALWRXOZ0KbE5XuW5LVzi9A/icpN8Cvwc+EhFDJ8I/RnYF1UTg4fQAWAHcL+lG4HngmhTfBMwDeoFfA++vZkXNzKwyIxaKiLiuRPieMn0fAB4o07YdeNnJ8Ig4CFxSIh7AkpHyMzOzseVvZpuZWSEXCjMzK+RCYWZmhVwozMyskAuFmZkVcqEwM7NCLhRmZlbIhcLMzAq5UJiZWSEXCjMzK+RCYWZmhar7wXczG7XpFd6boRb6VryrYcu2Vz7vUZiZWSEXCjMzK+RCYWZmhUZVKCStkXRA0u5c7ExJmyXtTc9npLgk3S2pV9IuSefnxixM/fdKWpiLz5HUk8bcne6CV3YZZmZWP6Pdo1gLdA6LLQe2RMQMYEt6DXAF2S1QZwCLgVWQfeiT3R3vQuAC4JbcB/8q4EO5cZ0jLMPMzOpkVIUiIh4FDg0LzwfWpel1wFW5+PrIbAMmSZoMXA5sjohDEXEY2Ax0prbXRMS2dFe79cPmVWoZZmZWJ8o+m0fRUZoObIyI89LrFyNiUpoWcDgiJknaCKyIiMdS2xZgGdl9t18VEben+GeBY0B36n9pil8ELIuIK8sto0Rui8n2XmhtbZ3T1dVVch0GBgZoaWkZ1fo2QjPn59wqk8+tp/9Iw/KYNeX0l8Xqsd0qXefWibD/WHXLLrXOtfBKeb8dr46Ojh0R0VaqrSbfo4iIkDS6ijMGy4iI1cBqgLa2tmhvby85j+7ubsq1NYNmzs+5VSaf26JGfo9iQfvLYvXYbpWu89JZg6zsqe7jqdQ618Ir5f1WS9Vc9bQ/HTYiPR9I8X5gWq7f1BQrik8tES9ahpmZ1Uk1hWIDMHTl0kLgwVz8hnT101zgSETsAx4BLpN0RjqJfRnwSGo7KmluOrx0w7B5lVqGmZnVyaj27STdS3aO4SxJL5BdvbQCuF/SjcDzwDWp+yZgHtAL/Bp4P0BEHJJ0G/B46ve5iBg6Qf4xsiurJgIPpwcFyzAzszoZVaGIiOvKNF1Som8AS8rMZw2wpkR8O3BeifjBUsswM7P68TezzcyskAuFmZkVcqEwM7NCLhRmZlbIhcLMzAq5UJiZWSEXCjMzK+RCYWZmhVwozMyskAuFmZkVcqEwM7NCLhRmZlbIhcLMzAq5UJiZWSEXCjMzK+RCYWZmhSouFJLeLGln7nFU0ick3SqpPxeflxtzs6ReSc9IujwX70yxXknLc/FzJP0gxe+TdErlq2pmZpWouFBExDMRMTsiZgNzyG57+t3UfNdQW0RsApA0E7gWOBfoBL4iaYKkCcCXgSuAmcB1qS/AnWlebwQOAzdWmq+ZmVWmVoeeLgGejYjnC/rMB7oi4qWI+CnZPbUvSI/eiHguIn4DdAHzJQm4GPh2Gr8OuKpG+ZqZ2Sgpu8V1lTOR1gBPRMSXJN0KLAKOAtuBpRFxWNKXgG0R8Y005h7g4TSLzoj4YIpfD1wI3Jr6vzHFpwEPR8TL7q0taTGwGKC1tXVOV1dXyTwHBgZoaWmpen3HSjPn59wqk8+tp/9Iw/KYNeX0l8Xqsd0qXefWibD/WHXLLrXOtfBKeb8dr46Ojh0R0Vaq7aSqsgLSeYN3Azen0CrgNiDS80rgA9Uup0hErAZWA7S1tUV7e3vJft3d3ZRrawbNnJ9zq0w+t0XLH2pYHn0L2l8Wq8d2q3Sdl84aZGVPdR9Ppda5Fl4p77daqrpQkJ1beCIi9gMMPQNI+hqwMb3sB6blxk1NMcrEDwKTJJ0UEYPD+luNTB/lP+SlswZr/kHXt+JdNZ2fmY2NWpyjuA64d+iFpMm5tvcAu9P0BuBaSadKOgeYAfwQeByYka5wOoXshPeGyI6JbQWuTuMXAg/WIF8zMzsOVe1RSDoNeCfw4Vz4byXNJjv01DfUFhF7JN0PPAUMAksi4ndpPjcBjwATgDURsSfNaxnQJel24EngnmryNTOz41dVoYiIXwGvHRa7vqD/HcAdJeKbgE0l4s+RXRVlZmYN4m9mm5lZIRcKMzMr5EJhZmaFXCjMzKyQC4WZmRVyoTAzs0IuFGZmVsiFwszMCrlQmJlZIRcKMzMr5EJhZmaFXCjMzKyQC4WZmRVyoTAzs0IuFGZmVqjqQiGpT1KPpJ2StqfYmZI2S9qbns9IcUm6W1KvpF2Szs/NZ2Hqv1fSwlx8Tpp/bxqranM2M7PRq9UeRUdEzI6ItvR6ObAlImYAW9JryO6vPSM9FgOrICsswC3AhWQ3KrplqLikPh/KjeusUc5mZjYKY3XoaT6wLk2vA67KxddHZhswKd1j+3Jgc0QciojDwGagM7W9JiK2pXtor8/Ny8zM6kDZ528VM5B+Chwmu0f2/4iI1ZJejIhJqV3A4YiYJGkjsCIiHkttW8jui90OvCoibk/xzwLHgO7U/9IUvwhYFhFXDsthMdkeCq2trXO6urpK5jowMEBLS0tV6zuWGpFfT/+RUfVrnQj7j9V22bOmnF6T+TTz3zWf22i39Vgota3rsd0qXedavN9q9f4a7pXyfjteHR0dO3JHhf5IVffMTt4eEf2S/gTYLOnH+caICEnVVaMRRMRqYDVAW1tbtLe3l+zX3d1NubZm0Ij8Fi1/aFT9ls4aZGVPLd4uf9C3oL0m82nmv2s+t9Fu67FQalvXY7tVus61eL/V6v013Cvl/VZLVR96ioj+9HwA+C7ZOYb96bAR6flA6t4PTMsNn5piRfGpJeJmZlYnVRUKSadJevXQNHAZsBvYAAxdubQQeDBNbwBuSFc/zQWORMQ+4BHgMklnpJPYlwGPpLajkuamQ1g35OZlZmZ1UO2xhFbgu+mK1ZOAb0XEP0l6HLhf0o3A88A1qf8mYB7QC/waeD9ARBySdBvweOr3uYg4lKY/BqwFJgIPp4eZWaHpY3Sob+mswcJDan0r3jUmy22kqgpFRDwHvKVE/CBwSYl4AEvKzGsNsKZEfDtwXjV5mplZ5fzNbDMzK+RCYWZmhVwozMyskAuFmZkVcqEwM7NCLhRmZlbIhcLMzArV9sd7zMzGubH6ot9orO08bUzm6z0KMzMr5EJhZmaFXCjMzKyQC4WZmRVyoTAzs0IuFGZmVsiFwszMClVcKCRNk7RV0lOS9kj6eIrfKqlf0s70mJcbc7OkXknPSLo8F+9MsV5Jy3PxcyT9IMXvk3RKpfmamVllqtmjGASWRsRMYC6wRNLM1HZXRMxOj00Aqe1a4FygE/iKpAmSJgBfBq4AZgLX5eZzZ5rXG4HDwI1V5GtmZhWo+JvZ6X7W+9L0LyU9DUwpGDIf6IqIl4CfSuoFLkhtvelueUjqAuan+V0MvC/1WQfcCqyqNOeRNPIblSfi7RPN7MSg7O6kVc5Emg48SnbL0k8Ci4CjwHayvY7Dkr4EbIuIb6Qx9/CH+193RsQHU/x64EKyorAt7U0gaRrwcES87LaokhYDiwFaW1vndHV1lcxzYGCAlpaWsuvR03/kONa6tmZNOX3E/MbCaNe5dSLsP1bbZc+acnpN5tOI7TZa+dwa/f4arh7brdJ1Hov3W600c27nnD6h4r9pR0fHjohoK9VW9W89SWoBHgA+ERFHJa0CbgMiPa8EPlDtcopExGpgNUBbW1u0t7eX7Nfd3U25NqDwhuljrW9B+4j5jYXRrvPSWYOs7KntT4P1LWivyXwasd1GK59bo99fw9Vju1W6zmPxfquVZs5tbedpY/I3rWptJZ1MViS+GRHfAYiI/bn2rwEb08t+YFpu+NQUo0z8IDBJ0kkRMTisv5mZ1Uk1Vz0JuAd4OiK+kItPznV7D7A7TW8ArpV0qqRzgBnAD4HHgRnpCqdTyE54b4jsmNhW4Oo0fiHwYKX5mplZZarZo3gbcD3QI2lnin2K7Kql2WSHnvqADwNExB5J9wNPkV0xtSQifgcg6SbgEWACsCYi9qT5LQO6JN0OPElWmMzMrI6querpMUAlmjYVjLkDuKNEfFOpcelKqAuGx+3EUKurzJbOGjyuY+G+wszs+Pib2WZmVsiFwszMCjXnNV5mY6ieX6w83sNiZs3IexRmZlbIhcLMzAq5UJiZWSEXCjMzK+RCYWZmhVwozMyskAuFmZkVcqEwM7NC/sJdk5i+/CF/OcvMmpL3KMzMrJALhZmZFfKhJ7NxoNTvW/lQp42W9yjMzKxQ0xcKSZ2SnpHUK2l5o/MxMxtvmrpQSJoAfBm4AphJdpvVmY3NysxsfGnqQkF2G9TeiHguIn4DdAHzG5yTmdm4oohodA5lSboa6IyID6bX1wMXRsRNw/otBhanl28Gnikzy7OAX4xRurXQzPk5t8o4t8o4t8pUk9vrI+LsUg0nxFVPEbEaWD1SP0nbI6KtDilVpJnzc26VcW6VcW6VGavcmv3QUz8wLfd6aoqZmVmdNHuheByYIekcSacA1wIbGpyTmdm40tSHniJiUNJNwCPABGBNROypYpYjHp5qsGbOz7lVxrlVxrlVZkxya+qT2WZm1njNfujJzMwazIXCzMwKjZtC0cw/BSKpT1KPpJ2Stjc4lzWSDkjanYudKWmzpL3p+Ywmyu1WSf1p2+2UNK9BuU2TtFXSU5L2SPp4ijd82xXk1vBtJ+lVkn4o6Ucpt79J8XMk/SD9e70vXczSLLmtlfTT3HabXe/ccjlOkPSkpI3p9dhst4g44R9kJ8KfBd4AnAL8CJjZ6Lxy+fUBZzU6j5TLO4Dzgd252N8Cy9P0cuDOJsrtVuC/NsF2mwycn6ZfDfyE7GdnGr7tCnJr+LYDBLSk6ZOBHwBzgfuBa1P8q8BHmyi3tcDVjX7Ppbw+CXwL2Jhej8l2Gy97FP4pkFGKiEeBQ8PC84F1aXodcFVdk0rK5NYUImJfRDyRpn8JPA1MoQm2XUFuDReZgfTy5PQI4GLg2yneqO1WLremIGkq8C7g79JrMUbbbbwUiinAz3KvX6BJ/qEkAfyzpB3p50iaTWtE7EvT/wq0NjKZEm6StCsdmmrIYbE8SdOB/0T2P9Cm2nbDcoMm2Hbp8MlO4ACwmWzv/8WIGExdGvbvdXhuETG03e5I2+0uSac2Ijfgi8BfAb9Pr1/LGG238VIomt3bI+J8sl/JXSLpHY1OqJzI9mmb5n9VwCrgPwCzgX3AykYmI6kFeAD4REQczbc1etuVyK0ptl1E/C4iZpP98sIFwJ81Io9Shucm6TzgZrIc3wqcCSyrd16SrgQORMSOeixvvBSKpv4pkIjoT88HgO+S/WNpJvslTQZIzwcanM//FxH70z/m3wNfo4HbTtLJZB/E34yI76RwU2y7Urk107ZL+bwIbAX+ApgkaegLwQ3/95rLrTMdyouIeAn4exqz3d4GvFtSH9mh9IuB/84YbbfxUiia9qdAJJ0m6dVD08BlwO7iUXW3AViYphcCDzYwlz8y9CGcvIcGbbt0fPge4OmI+EKuqeHbrlxuzbDtJJ0taVKangi8k+wcylbg6tStUdutVG4/zhV+kZ0DqPt2i4ibI2JqREwn+zz7XkQsYKy2W6PP2tfrAcwju9rjWeDTjc4nl9cbyK7C+hGwp9G5AfeSHYb4LdkxzhvJjn1uAfYC/ws4s4ly+zrQA+wi+1Ce3KDc3k52WGkXsDM95jXDtivIreHbDvhz4MmUw27gr1P8DcAPgV7gH4BTmyi376Xtthv4BunKqEY9gHb+cNXTmGw3/4SHmZkVGi+HnszMrEIuFGZmVsiFwszMCrlQmJlZIRcKMzMr5EJhZmaFXCjMzKzQ/wMrK1RDN6nH+QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_test.hist(column='soil_type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153
    },
    "colab_type": "code",
    "id": "JYD2cx-TxVIJ",
    "outputId": "908c03c0-be0d-41e7-e659-b5f92918503d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    14165\n",
       "7    10592\n",
       "6     1788\n",
       "1     1025\n",
       "3      868\n",
       "2      475\n",
       "4      137\n",
       "Name: Cover_Type, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#as train and test are a exact match the distribution of train and test should be assumed to be same too. thus no need for oversampling of minority classes\n",
    "#the data must have been split in stratified manner to form train test\n",
    "df_train.Cover_Type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "colab_type": "code",
    "id": "e0G76GxZxssf",
    "outputId": "519ce33e-a4cc-44c4-f5c6-abe6160a3541"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Elevation(meters)</th>\n",
       "      <th>Aspect(degrees)</th>\n",
       "      <th>Slope(degrees)</th>\n",
       "      <th>Horizontal_Distance_To_Hydrology(meters)</th>\n",
       "      <th>Vertical_Distance_To_Hydrology(meters)</th>\n",
       "      <th>Horizontal_Distance_To_Roadways(meters)</th>\n",
       "      <th>Hillshade_9am</th>\n",
       "      <th>Hillshade_Noon</th>\n",
       "      <th>Hillshade_3pm</th>\n",
       "      <th>Horizontal_Distance_To_Fire_Points(meters)</th>\n",
       "      <th>Wilderness_Area_1</th>\n",
       "      <th>Wilderness_Area_2</th>\n",
       "      <th>Wilderness_Area_3</th>\n",
       "      <th>Wilderness_Area_4</th>\n",
       "      <th>Soil_Type_1</th>\n",
       "      <th>Soil_Type_2</th>\n",
       "      <th>Soil_Type_3</th>\n",
       "      <th>Soil_Type_4</th>\n",
       "      <th>Soil_Type_5</th>\n",
       "      <th>Soil_Type_6</th>\n",
       "      <th>Soil_Type_7</th>\n",
       "      <th>Soil_Type_8</th>\n",
       "      <th>Soil_Type_9</th>\n",
       "      <th>Soil_Type_10</th>\n",
       "      <th>Soil_Type_11</th>\n",
       "      <th>Soil_Type_12</th>\n",
       "      <th>Soil_Type_13</th>\n",
       "      <th>Soil_Type_14</th>\n",
       "      <th>Soil_Type_15</th>\n",
       "      <th>Soil_Type_16</th>\n",
       "      <th>Soil_Type_17</th>\n",
       "      <th>Soil_Type_18</th>\n",
       "      <th>Soil_Type_19</th>\n",
       "      <th>Soil_Type_20</th>\n",
       "      <th>Soil_Type_21</th>\n",
       "      <th>Soil_Type_22</th>\n",
       "      <th>Soil_Type_23</th>\n",
       "      <th>Soil_Type_24</th>\n",
       "      <th>Soil_Type_25</th>\n",
       "      <th>Soil_Type_26</th>\n",
       "      <th>Soil_Type_27</th>\n",
       "      <th>Soil_Type_28</th>\n",
       "      <th>Soil_Type_29</th>\n",
       "      <th>Soil_Type_30</th>\n",
       "      <th>Soil_Type_31</th>\n",
       "      <th>Soil_Type_32</th>\n",
       "      <th>Soil_Type_33</th>\n",
       "      <th>Soil_Type_34</th>\n",
       "      <th>Soil_Type_35</th>\n",
       "      <th>Soil_Type_36</th>\n",
       "      <th>Soil_Type_37</th>\n",
       "      <th>Soil_Type_38</th>\n",
       "      <th>Soil_Type_39</th>\n",
       "      <th>Soil_Type_40</th>\n",
       "      <th>Cover_Type</th>\n",
       "      <th>soil_type</th>\n",
       "      <th>wilderness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.0</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2959.328330</td>\n",
       "      <td>155.720241</td>\n",
       "      <td>14.028537</td>\n",
       "      <td>269.220792</td>\n",
       "      <td>46.332737</td>\n",
       "      <td>2346.340241</td>\n",
       "      <td>212.042134</td>\n",
       "      <td>223.446850</td>\n",
       "      <td>142.854630</td>\n",
       "      <td>1971.574871</td>\n",
       "      <td>0.450843</td>\n",
       "      <td>0.050327</td>\n",
       "      <td>0.435422</td>\n",
       "      <td>0.063408</td>\n",
       "      <td>0.004819</td>\n",
       "      <td>0.011945</td>\n",
       "      <td>0.007917</td>\n",
       "      <td>0.021480</td>\n",
       "      <td>0.002719</td>\n",
       "      <td>0.012771</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0.000516</td>\n",
       "      <td>0.001997</td>\n",
       "      <td>0.055077</td>\n",
       "      <td>0.020861</td>\n",
       "      <td>0.051566</td>\n",
       "      <td>0.030809</td>\n",
       "      <td>0.001170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004750</td>\n",
       "      <td>0.006231</td>\n",
       "      <td>0.003305</td>\n",
       "      <td>0.006850</td>\n",
       "      <td>0.017005</td>\n",
       "      <td>0.001274</td>\n",
       "      <td>0.057212</td>\n",
       "      <td>0.097074</td>\n",
       "      <td>0.036730</td>\n",
       "      <td>0.000585</td>\n",
       "      <td>0.004303</td>\n",
       "      <td>0.001824</td>\n",
       "      <td>0.001824</td>\n",
       "      <td>0.200172</td>\n",
       "      <td>0.051842</td>\n",
       "      <td>0.042065</td>\n",
       "      <td>0.091738</td>\n",
       "      <td>0.078967</td>\n",
       "      <td>0.002719</td>\n",
       "      <td>0.003064</td>\n",
       "      <td>0.000207</td>\n",
       "      <td>0.000379</td>\n",
       "      <td>0.027539</td>\n",
       "      <td>0.023821</td>\n",
       "      <td>0.014561</td>\n",
       "      <td>5.536110</td>\n",
       "      <td>24.391842</td>\n",
       "      <td>2.111394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>277.578227</td>\n",
       "      <td>112.109417</td>\n",
       "      <td>7.458200</td>\n",
       "      <td>212.406921</td>\n",
       "      <td>58.938186</td>\n",
       "      <td>1558.148732</td>\n",
       "      <td>26.678889</td>\n",
       "      <td>19.610671</td>\n",
       "      <td>38.000582</td>\n",
       "      <td>1321.145310</td>\n",
       "      <td>0.497586</td>\n",
       "      <td>0.218623</td>\n",
       "      <td>0.495821</td>\n",
       "      <td>0.243699</td>\n",
       "      <td>0.069255</td>\n",
       "      <td>0.108640</td>\n",
       "      <td>0.088628</td>\n",
       "      <td>0.144981</td>\n",
       "      <td>0.052078</td>\n",
       "      <td>0.112287</td>\n",
       "      <td>0.017599</td>\n",
       "      <td>0.022718</td>\n",
       "      <td>0.044639</td>\n",
       "      <td>0.228135</td>\n",
       "      <td>0.142920</td>\n",
       "      <td>0.221153</td>\n",
       "      <td>0.172803</td>\n",
       "      <td>0.034192</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.068761</td>\n",
       "      <td>0.078689</td>\n",
       "      <td>0.057392</td>\n",
       "      <td>0.082484</td>\n",
       "      <td>0.129293</td>\n",
       "      <td>0.035666</td>\n",
       "      <td>0.232251</td>\n",
       "      <td>0.296064</td>\n",
       "      <td>0.188101</td>\n",
       "      <td>0.024184</td>\n",
       "      <td>0.065457</td>\n",
       "      <td>0.042675</td>\n",
       "      <td>0.042675</td>\n",
       "      <td>0.400136</td>\n",
       "      <td>0.221711</td>\n",
       "      <td>0.200742</td>\n",
       "      <td>0.288661</td>\n",
       "      <td>0.269692</td>\n",
       "      <td>0.052078</td>\n",
       "      <td>0.055267</td>\n",
       "      <td>0.014370</td>\n",
       "      <td>0.019456</td>\n",
       "      <td>0.163650</td>\n",
       "      <td>0.152494</td>\n",
       "      <td>0.119790</td>\n",
       "      <td>1.438261</td>\n",
       "      <td>9.467772</td>\n",
       "      <td>1.061851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1879.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-166.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2809.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1103.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>213.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>1020.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2995.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1984.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>226.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>1704.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3161.000000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>390.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>3329.750000</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>2536.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3844.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>1320.000000</td>\n",
       "      <td>589.000000</td>\n",
       "      <td>7087.000000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>251.000000</td>\n",
       "      <td>7142.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Elevation(meters)  Aspect(degrees)  ...     soil_type    wilderness\n",
       "count       29050.000000     29050.000000  ...  29050.000000  29050.000000\n",
       "mean         2959.328330       155.720241  ...     24.391842      2.111394\n",
       "std           277.578227       112.109417  ...      9.467772      1.061851\n",
       "min          1879.000000         0.000000  ...      1.000000      1.000000\n",
       "25%          2809.000000        58.000000  ...     20.000000      1.000000\n",
       "50%          2995.000000       128.000000  ...     29.000000      2.000000\n",
       "75%          3161.000000       262.000000  ...     31.000000      3.000000\n",
       "max          3844.000000       360.000000  ...     40.000000      4.000000\n",
       "\n",
       "[8 rows x 57 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the uncanny similarity between test and train even more prominent below\n",
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "colab_type": "code",
    "id": "t4gAODHIx0ES",
    "outputId": "9de2db20-5589-43bb-f9ba-530143bd8488"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Elevation(meters)</th>\n",
       "      <th>Aspect(degrees)</th>\n",
       "      <th>Slope(degrees)</th>\n",
       "      <th>Horizontal_Distance_To_Hydrology(meters)</th>\n",
       "      <th>Vertical_Distance_To_Hydrology(meters)</th>\n",
       "      <th>Horizontal_Distance_To_Roadways(meters)</th>\n",
       "      <th>Hillshade_9am</th>\n",
       "      <th>Hillshade_Noon</th>\n",
       "      <th>Hillshade_3pm</th>\n",
       "      <th>Horizontal_Distance_To_Fire_Points(meters)</th>\n",
       "      <th>Wilderness_Area_1</th>\n",
       "      <th>Wilderness_Area_2</th>\n",
       "      <th>Wilderness_Area_3</th>\n",
       "      <th>Wilderness_Area_4</th>\n",
       "      <th>Soil_Type_1</th>\n",
       "      <th>Soil_Type_2</th>\n",
       "      <th>Soil_Type_3</th>\n",
       "      <th>Soil_Type_4</th>\n",
       "      <th>Soil_Type_5</th>\n",
       "      <th>Soil_Type_6</th>\n",
       "      <th>Soil_Type_7</th>\n",
       "      <th>Soil_Type_8</th>\n",
       "      <th>Soil_Type_9</th>\n",
       "      <th>Soil_Type_10</th>\n",
       "      <th>Soil_Type_11</th>\n",
       "      <th>Soil_Type_12</th>\n",
       "      <th>Soil_Type_13</th>\n",
       "      <th>Soil_Type_14</th>\n",
       "      <th>Soil_Type_15</th>\n",
       "      <th>Soil_Type_16</th>\n",
       "      <th>Soil_Type_17</th>\n",
       "      <th>Soil_Type_18</th>\n",
       "      <th>Soil_Type_19</th>\n",
       "      <th>Soil_Type_20</th>\n",
       "      <th>Soil_Type_21</th>\n",
       "      <th>Soil_Type_22</th>\n",
       "      <th>Soil_Type_23</th>\n",
       "      <th>Soil_Type_24</th>\n",
       "      <th>Soil_Type_25</th>\n",
       "      <th>Soil_Type_26</th>\n",
       "      <th>Soil_Type_27</th>\n",
       "      <th>Soil_Type_28</th>\n",
       "      <th>Soil_Type_29</th>\n",
       "      <th>Soil_Type_30</th>\n",
       "      <th>Soil_Type_31</th>\n",
       "      <th>Soil_Type_32</th>\n",
       "      <th>Soil_Type_33</th>\n",
       "      <th>Soil_Type_34</th>\n",
       "      <th>Soil_Type_35</th>\n",
       "      <th>Soil_Type_36</th>\n",
       "      <th>Soil_Type_37</th>\n",
       "      <th>Soil_Type_38</th>\n",
       "      <th>Soil_Type_39</th>\n",
       "      <th>Soil_Type_40</th>\n",
       "      <th>soil_type</th>\n",
       "      <th>wilderness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.00000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.00000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "      <td>551962.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2959.367246</td>\n",
       "      <td>155.653469</td>\n",
       "      <td>14.107660</td>\n",
       "      <td>269.439133</td>\n",
       "      <td>46.423388</td>\n",
       "      <td>2350.346942</td>\n",
       "      <td>212.151518</td>\n",
       "      <td>223.311973</td>\n",
       "      <td>142.511086</td>\n",
       "      <td>1980.749972</td>\n",
       "      <td>0.448761</td>\n",
       "      <td>0.051493</td>\n",
       "      <td>0.436108</td>\n",
       "      <td>0.063638</td>\n",
       "      <td>0.005238</td>\n",
       "      <td>0.013005</td>\n",
       "      <td>0.008321</td>\n",
       "      <td>0.021328</td>\n",
       "      <td>0.00275</td>\n",
       "      <td>0.011240</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>0.001973</td>\n",
       "      <td>0.056225</td>\n",
       "      <td>0.021386</td>\n",
       "      <td>0.051585</td>\n",
       "      <td>0.029959</td>\n",
       "      <td>0.001024</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.004904</td>\n",
       "      <td>0.005872</td>\n",
       "      <td>0.003267</td>\n",
       "      <td>0.006924</td>\n",
       "      <td>0.01588</td>\n",
       "      <td>0.001451</td>\n",
       "      <td>0.057451</td>\n",
       "      <td>0.099521</td>\n",
       "      <td>0.036617</td>\n",
       "      <td>0.000828</td>\n",
       "      <td>0.004464</td>\n",
       "      <td>0.001872</td>\n",
       "      <td>0.001618</td>\n",
       "      <td>0.198260</td>\n",
       "      <td>0.051931</td>\n",
       "      <td>0.044286</td>\n",
       "      <td>0.090321</td>\n",
       "      <td>0.077650</td>\n",
       "      <td>0.002776</td>\n",
       "      <td>0.003265</td>\n",
       "      <td>0.000205</td>\n",
       "      <td>0.000520</td>\n",
       "      <td>0.026765</td>\n",
       "      <td>0.023759</td>\n",
       "      <td>0.015086</td>\n",
       "      <td>24.360896</td>\n",
       "      <td>2.114624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>280.111066</td>\n",
       "      <td>111.903513</td>\n",
       "      <td>7.489805</td>\n",
       "      <td>212.557036</td>\n",
       "      <td>58.261247</td>\n",
       "      <td>1559.314218</td>\n",
       "      <td>26.774683</td>\n",
       "      <td>19.776974</td>\n",
       "      <td>38.288850</td>\n",
       "      <td>1324.355138</td>\n",
       "      <td>0.497368</td>\n",
       "      <td>0.221001</td>\n",
       "      <td>0.495901</td>\n",
       "      <td>0.244108</td>\n",
       "      <td>0.072182</td>\n",
       "      <td>0.113294</td>\n",
       "      <td>0.090841</td>\n",
       "      <td>0.144474</td>\n",
       "      <td>0.05237</td>\n",
       "      <td>0.105421</td>\n",
       "      <td>0.013187</td>\n",
       "      <td>0.017235</td>\n",
       "      <td>0.044374</td>\n",
       "      <td>0.230356</td>\n",
       "      <td>0.144666</td>\n",
       "      <td>0.221188</td>\n",
       "      <td>0.170473</td>\n",
       "      <td>0.031978</td>\n",
       "      <td>0.002331</td>\n",
       "      <td>0.069859</td>\n",
       "      <td>0.076402</td>\n",
       "      <td>0.057060</td>\n",
       "      <td>0.082924</td>\n",
       "      <td>0.12501</td>\n",
       "      <td>0.038067</td>\n",
       "      <td>0.232703</td>\n",
       "      <td>0.299361</td>\n",
       "      <td>0.187819</td>\n",
       "      <td>0.028762</td>\n",
       "      <td>0.066664</td>\n",
       "      <td>0.043220</td>\n",
       "      <td>0.040190</td>\n",
       "      <td>0.398689</td>\n",
       "      <td>0.221888</td>\n",
       "      <td>0.205729</td>\n",
       "      <td>0.286642</td>\n",
       "      <td>0.267621</td>\n",
       "      <td>0.052610</td>\n",
       "      <td>0.057044</td>\n",
       "      <td>0.014307</td>\n",
       "      <td>0.022797</td>\n",
       "      <td>0.161395</td>\n",
       "      <td>0.152297</td>\n",
       "      <td>0.121896</td>\n",
       "      <td>9.486339</td>\n",
       "      <td>1.061266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1859.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-173.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2809.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1106.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>213.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>1024.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2996.000000</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1998.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>226.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>1710.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3164.000000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>384.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>3328.000000</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>2550.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3858.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>1397.000000</td>\n",
       "      <td>601.000000</td>\n",
       "      <td>7117.000000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>7173.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Elevation(meters)  Aspect(degrees)  ...      soil_type     wilderness\n",
       "count      551962.000000    551962.000000  ...  551962.000000  551962.000000\n",
       "mean         2959.367246       155.653469  ...      24.360896       2.114624\n",
       "std           280.111066       111.903513  ...       9.486339       1.061266\n",
       "min          1859.000000         0.000000  ...       1.000000       1.000000\n",
       "25%          2809.000000        58.000000  ...      20.000000       1.000000\n",
       "50%          2996.000000       127.000000  ...      29.000000       2.000000\n",
       "75%          3164.000000       260.000000  ...      31.000000       3.000000\n",
       "max          3858.000000       360.000000  ...      40.000000       4.000000\n",
       "\n",
       "[8 rows x 56 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8FnBAyrm5o6N"
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#feat engineering\n",
    "#ENCODINGS\n",
    "\n",
    "\n",
    "df=pd.DataFrame(df_train.groupby(['wilderness','soil_type'])['soil_type'].count()/df_train.groupby('wilderness')['wilderness'].count())\n",
    "df=df.reset_index()\n",
    "df.rename(columns={0:'fe'},inplace=True)\n",
    "df['flora']=df[['wilderness','soil_type']].apply(lambda x: '_'.join(x.astype('str')), axis = 1)\n",
    "encode = dict(zip(df.flora, df.fe))\n",
    "df_train['flora']=df_train[['wilderness','soil_type']].apply(lambda x: '_'.join(x.astype('str')), axis = 1)\n",
    "df_train['soil_fe']=df_train.flora.map(encode)\n",
    "df_train['soil_fe']=df_train['soil_fe']*100\n",
    "df_train.drop(['flora'],axis=1,inplace=True)\n",
    "\n",
    "\n",
    "df=pd.DataFrame(df_test.groupby(['wilderness','soil_type'])['soil_type'].count()/df_test.groupby('wilderness')['wilderness'].count())\n",
    "df=df.reset_index()\n",
    "df.rename(columns={0:'fe'},inplace=True)\n",
    "df['flora']=df[['wilderness','soil_type']].apply(lambda x: '_'.join(x.astype('str')), axis = 1)\n",
    "encode = dict(zip(df.flora, df.fe))\n",
    "df_test['flora']=df_test[['wilderness','soil_type']].apply(lambda x: '_'.join(x.astype('str')), axis = 1)\n",
    "df_test['soil_fe']=df_test.flora.map(encode)\n",
    "df_test['soil_fe']=df_test['soil_fe']*100\n",
    "df_test.drop(['flora'],axis=1,inplace=True)\n",
    "\n",
    "\n",
    "\n",
    "#NEW FEATS\n",
    "df_train['hypotenuse']=np.sqrt(df_train['Horizontal_Distance_To_Hydrology(meters)']**2+df_train['Vertical_Distance_To_Hydrology(meters)']**2)\n",
    "df_train['hill_9_sq']=df_train['Hillshade_9am']**2\n",
    "df_train['hill_noon_sq']=df_train['Hillshade_Noon']**2\n",
    "df_train['Elevation(meters)_hd_hdg']=df_train['Elevation(meters)']-0.2*df_train['Horizontal_Distance_To_Hydrology(meters)']\n",
    "df_train['Elevation(meters)_hd_road']=df_train['Elevation(meters)']-0.5*df_train['Horizontal_Distance_To_Roadways(meters)']\n",
    "df_train['Elevation(meters)_vd_hdg']=df_train['Elevation(meters)']-df_train['Vertical_Distance_To_Hydrology(meters)']\n",
    "df_train['mean_amenities']=(df_train['Horizontal_Distance_To_Hydrology(meters)']+df_train['Horizontal_Distance_To_Roadways(meters)']+\n",
    "df_train['Horizontal_Distance_To_Fire_Points(meters)'])/3\n",
    "df_train['mean_amenities_fire_water']=(df_train['Horizontal_Distance_To_Hydrology(meters)']+df_train['Horizontal_Distance_To_Fire_Points(meters)'])/2\n",
    "\n",
    "\n",
    "df_test['hypotenuse']=np.sqrt(df_test['Horizontal_Distance_To_Hydrology(meters)']**2+df_test['Vertical_Distance_To_Hydrology(meters)']**2)\n",
    "df_test['hill_9_sq']=df_test['Hillshade_9am']**2\n",
    "df_test['hill_noon_sq']=df_test['Hillshade_Noon']**2\n",
    "df_test['Elevation(meters)_hd_hdg']=df_test['Elevation(meters)']-0.2*df_test['Horizontal_Distance_To_Hydrology(meters)']\n",
    "df_test['Elevation(meters)_hd_road']=df_test['Elevation(meters)']-0.5*df_test['Horizontal_Distance_To_Roadways(meters)']\n",
    "df_test['Elevation(meters)_vd_hdg']=df_test['Elevation(meters)']-df_test['Vertical_Distance_To_Hydrology(meters)']\n",
    "df_test['mean_amenities']=(df_test['Horizontal_Distance_To_Hydrology(meters)']+df_test['Horizontal_Distance_To_Roadways(meters)']+\n",
    "df_test['Horizontal_Distance_To_Fire_Points(meters)'])/3\n",
    "df_test['mean_amenities_fire_water']=(df_test['Horizontal_Distance_To_Hydrology(meters)']+df_test['Horizontal_Distance_To_Fire_Points(meters)'])/2\n",
    "\n",
    "\n",
    "soil_family={1:'cathedral',2:'vanet',3:'Haploborolis',4:'Ratake',5:'vanet',6:'vanet',7:'Gothic',8:'Supervisor',9:'Troutville',10:'Bullwark',11:'Bullwark',\n",
    "12:'Legault',13:'Catamount',14:'Pachic',15:'Cryaquolis',16:'Cryaquolis',17:'Gateview',18:'Rogert',19:'Typic',20:'Typic',21:'Typic',\n",
    "22:'Leighcan',23:'Leighcan',24:'Leighcan',25:'Leighcan',26:'Granile',27:'Leighcan',28:'Leighcan',29:'como',30:'como',31:'Leighcan',32:'Catamount',33:'Leighcan',\n",
    "34:'Cryorthents',35:'Cryumbrepts',36:'Bross',37:'Rock',38:'Leighcan',39:'Moran',40:'Moran'}\n",
    "df_train['soil_fam']=df_train.soil_type.map(soil_family)\n",
    "df_test['soil_fam']=df_test.soil_type.map(soil_family)\n",
    "\n",
    "df_log=df_train.drop(['soil_type','wilderness','soil_fam','Cover_Type'],axis=1)\n",
    "df_log_test=df_test.drop(['soil_type','wilderness','soil_fam'],axis=1)\n",
    "#scaling\n",
    "scaled=df_log\n",
    "scaled_test=df_log_test\n",
    "\n",
    "#reverse clustering fitted on test prediction on train\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "df2_scaled = min_max_scaler.fit_transform(scaled)\n",
    "bad_indices = np.where(np.isnan(df2_scaled))\n",
    "df2_scaled[bad_indices] = 0\n",
    "\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "df2_scaled_test = min_max_scaler.fit_transform(scaled_test)\n",
    "bad_indices = np.where(np.isnan(df2_scaled_test))\n",
    "df2_scaled_test[bad_indices] = 0\n",
    "# #kmeans\n",
    "kmeans = KMeans(n_clusters=7, init='k-means++')\n",
    "# fitting the k means algorithm on scaled data\n",
    "kmeans.fit(df2_scaled_test)\n",
    "pred=kmeans.predict(df2_scaled_test)\n",
    "df_test['tree_cluster']=pred\n",
    "pred2=kmeans.predict(df2_scaled)\n",
    "df_train['tree_cluster']=pred2\n",
    "\n",
    "#simple mode target encodings, once check with them score once without them score(preferably should be used with kfold strategy)\n",
    "columns=['soil_type','soil_fam','wilderness']\n",
    "df=pd.DataFrame(df_train.groupby(columns)['Cover_Type'].agg(pd.Series.mode))\n",
    "df=df.reset_index()\n",
    "df['te1']=df[columns].apply(lambda x: '_'.join(x.astype('str')), axis = 1)\n",
    "encode = dict(zip(df.te1, df.Cover_Type))\n",
    "df_train['te1']=df_train[columns].apply(lambda x: '_'.join(x.astype('str')), axis = 1)\n",
    "df_test['te1']=df_test[columns].apply(lambda x: '_'.join(x.astype('str')), axis = 1)\n",
    "df_train['target_encode4']=df_train.te1.map(encode)\n",
    "df_test['target_encode4']=df_test.te1.map(encode)\n",
    "df_test['target_encode4'].fillna(df_test['target_encode4'].mean(),inplace=True)\n",
    "df_train.drop(['te1'],axis=1,inplace=True)\n",
    "\n",
    "#tree models prefer label encodings over onehot generally\n",
    "df_train=df_train.drop(['Soil_Type_1', 'Soil_Type_2', 'Soil_Type_3', 'Soil_Type_4',\n",
    "       'Soil_Type_5', 'Soil_Type_6', 'Soil_Type_7', 'Soil_Type_8',\n",
    "       'Soil_Type_9', 'Soil_Type_10', 'Soil_Type_11', 'Soil_Type_12',\n",
    "       'Soil_Type_13', 'Soil_Type_14', 'Soil_Type_15', 'Soil_Type_16',\n",
    "       'Soil_Type_17', 'Soil_Type_18', 'Soil_Type_19', 'Soil_Type_20',\n",
    "       'Soil_Type_21', 'Soil_Type_22', 'Soil_Type_23', 'Soil_Type_24',\n",
    "       'Soil_Type_25', 'Soil_Type_26', 'Soil_Type_27', 'Soil_Type_28',\n",
    "       'Soil_Type_29', 'Soil_Type_30', 'Soil_Type_31', 'Soil_Type_32',\n",
    "       'Soil_Type_33', 'Soil_Type_34', 'Soil_Type_35', 'Soil_Type_36',\n",
    "       'Soil_Type_37', 'Soil_Type_38', 'Soil_Type_39', 'Soil_Type_40'],axis=1)\n",
    "\n",
    "#to be used for catboost\n",
    "df_train['slope_fe_int']=df_train['soil_fe'].astype('int16')\n",
    "df_train['hypotenuse_int']=df_train['hypotenuse'].astype('int16')\n",
    "df_train['mean_amenities_int']=df_train['mean_amenities'].astype('int16')\n",
    "df_train['mean_amenities_fire_water_int']=df_train['mean_amenities_fire_water'].astype('int16')\n",
    "df_train['Elevation(meters)_hd_hdg_int']=df_train['Elevation(meters)_hd_hdg'].astype('int16')\n",
    "df_train['Elevation(meters)_hd_road_int']=df_train['Elevation(meters)_hd_road'].astype('int16')\n",
    "df_train['Elevation(meters)_hd_road_int']=df_train['Elevation(meters)_hd_road'].astype('int16')\n",
    "\n",
    "df_test['slope_fe_int']=df_test['soil_fe'].astype('int16')\n",
    "df_test['hypotenuse_int']=df_test['hypotenuse'].astype('int16')\n",
    "df_test['mean_amenities_int']=df_test['mean_amenities'].astype('int16')\n",
    "df_test['mean_amenities_fire_water_int']=df_test['mean_amenities_fire_water'].astype('int16')\n",
    "df_test['Elevation(meters)_hd_hdg_int']=df_test['Elevation(meters)_hd_hdg'].astype('int16')\n",
    "df_test['Elevation(meters)_hd_road_int']=df_test['Elevation(meters)_hd_road'].astype('int16')\n",
    "df_test['Elevation(meters)_hd_road_int']=df_test['Elevation(meters)_hd_road'].astype('int16')\n",
    "\n",
    "Y=df_train.Cover_Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 731
    },
    "colab_type": "code",
    "id": "EUDLlQ3NJBW6",
    "outputId": "53da9da8-56f4-41f7-d7ce-c9d1fa1fc652"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 29050 entries, 0 to 29049\n",
      "Data columns (total 35 columns):\n",
      " #   Column                                      Non-Null Count  Dtype  \n",
      "---  ------                                      --------------  -----  \n",
      " 0   Elevation(meters)                           29050 non-null  int64  \n",
      " 1   Aspect(degrees)                             29050 non-null  int64  \n",
      " 2   Slope(degrees)                              29050 non-null  int64  \n",
      " 3   Horizontal_Distance_To_Hydrology(meters)    29050 non-null  int64  \n",
      " 4   Vertical_Distance_To_Hydrology(meters)      29050 non-null  int64  \n",
      " 5   Horizontal_Distance_To_Roadways(meters)     29050 non-null  int64  \n",
      " 6   Hillshade_9am                               29050 non-null  int64  \n",
      " 7   Hillshade_Noon                              29050 non-null  int64  \n",
      " 8   Hillshade_3pm                               29050 non-null  int64  \n",
      " 9   Horizontal_Distance_To_Fire_Points(meters)  29050 non-null  int64  \n",
      " 10  Wilderness_Area_1                           29050 non-null  int64  \n",
      " 11  Wilderness_Area_2                           29050 non-null  int64  \n",
      " 12  Wilderness_Area_3                           29050 non-null  int64  \n",
      " 13  Wilderness_Area_4                           29050 non-null  int64  \n",
      " 14  Cover_Type                                  29050 non-null  int64  \n",
      " 15  soil_type                                   29050 non-null  int64  \n",
      " 16  wilderness                                  29050 non-null  int64  \n",
      " 17  soil_fe                                     29050 non-null  float64\n",
      " 18  hypotenuse                                  29050 non-null  float64\n",
      " 19  hill_9_sq                                   29050 non-null  int64  \n",
      " 20  hill_noon_sq                                29050 non-null  int64  \n",
      " 21  Elevation(meters)_hd_hdg                    29050 non-null  float64\n",
      " 22  Elevation(meters)_hd_road                   29050 non-null  float64\n",
      " 23  Elevation(meters)_vd_hdg                    29050 non-null  int64  \n",
      " 24  mean_amenities                              29050 non-null  float64\n",
      " 25  mean_amenities_fire_water                   29050 non-null  float64\n",
      " 26  soil_fam                                    29050 non-null  object \n",
      " 27  tree_cluster                                29050 non-null  int32  \n",
      " 28  target_encode4                              29050 non-null  int64  \n",
      " 29  slope_fe_int                                29050 non-null  int16  \n",
      " 30  hypotenuse_int                              29050 non-null  int16  \n",
      " 31  mean_amenities_int                          29050 non-null  int16  \n",
      " 32  mean_amenities_fire_water_int               29050 non-null  int16  \n",
      " 33  Elevation(meters)_hd_hdg_int                29050 non-null  int16  \n",
      " 34  Elevation(meters)_hd_road_int               29050 non-null  int16  \n",
      "dtypes: float64(6), int16(6), int32(1), int64(21), object(1)\n",
      "memory usage: 6.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2IoR4iRM6PiH"
   },
   "outputs": [],
   "source": [
    "#main\n",
    "\n",
    "#6000\n",
    "catboost_model2 = CatBoostClassifier(cat_features=[14,15,16,17,18],task_type='GPU',verbose=0,iterations=6000,\n",
    "bagging_temperature=0.41010395885331385,border_count=186,depth=8,\n",
    "l2_leaf_reg=21,learning_rate=0.1673344419215237,random_strength=3.230824361824754e-06,one_hot_max_size=20)\n",
    "\n",
    "#after deploying extensive feature selection process\n",
    "cat_columns=['Horizontal_Distance_To_Hydrology(meters)',\n",
    "       'Horizontal_Distance_To_Roadways(meters)', 'Hillshade_9am',\n",
    "       'Hillshade_Noon', 'Horizontal_Distance_To_Fire_Points(meters)',\n",
    "       'hill_9_sq', 'hill_noon_sq', 'Elevation(meters)_vd_hdg', 'slope_fe_int',\n",
    "       'hypotenuse_int', 'mean_amenities_int', 'mean_amenities_fire_water_int',\n",
    "       'Elevation(meters)_hd_hdg_int', 'Elevation(meters)_hd_road_int',\n",
    "       'soil_type','wilderness','soil_fam','tree_cluster','target_encode4']\n",
    "\n",
    "TRAIN_FINAL=df_train[cat_columns]\n",
    "TEST_FINAL=df_test[cat_columns]\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "G4CCY_l76WMy",
    "outputId": "0dc5454a-c9f2-4a0d-f35b-e5245a146079"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.23905594509883574\n",
      "0.23701235799933873\n",
      "0.24129861454725665\n",
      "0.23615249557035403\n",
      "0.23819590569546958\n",
      "Average:  0.23834306378225095\n"
     ]
    }
   ],
   "source": [
    "i=0\n",
    "l=[]\n",
    "fold = StratifiedKFold(n_splits=5,random_state=True)\n",
    "scores=[]\n",
    "i=1\n",
    "cnf_matrix=[]\n",
    "# kfold, scores = KFold(n_splits=5, shuffle=True, random_state=0), list()\n",
    "for train, test in fold.split(TRAIN_FINAL,Y):\n",
    "    x_train, x_test = TRAIN_FINAL.values[train], TRAIN_FINAL.values[test]\n",
    "    y_train, y_test = Y.values[train], Y.values[test] \n",
    "    model = catboost_model2\n",
    "    # eval_dataset = Pool(data=X_val,\n",
    "    #                 label=Y_val,\n",
    "    #                 cat_features=[17,18,19])\n",
    "    model.fit(x_train, y_train)\n",
    "    preds = model.predict_proba(x_test)\n",
    "\n",
    "    # for classification only\n",
    "    # preds2 = model.predict(x_train)\n",
    "    # cnf_matrix.append(confusion_matrix(y_test, preds2,labels=[1,2,3,4,5,6,7]))\n",
    "    # ######################\n",
    "    feature_importances = pd.DataFrame(model.feature_importances_,\n",
    "                                       index = TRAIN_FINAL.columns,\n",
    "                                        columns=['importance'])\n",
    "    sum=feature_importances.values\n",
    "    l.append(sum)\n",
    "    score = log_loss(y_test, preds)\n",
    "    scores.append(score)\n",
    "    print(score)\n",
    "    i+=1\n",
    "print(\"Average: \", np.sum(scores)/len(scores))\n",
    "##for classification only\n",
    "cm=0\n",
    "for item in cnf_matrix:\n",
    "  try:\n",
    "    cm=cm+item\n",
    "  except:\n",
    "    cm=item\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_4tRMb_4JwTN"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Re-_YwzMHEzN",
    "outputId": "8f18b49a-4cc5-434e-c621-dcef13018e8a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 79,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "train_set=TRAIN_FINAL\n",
    "labels=Y\n",
    "add=0\n",
    "for item in l:\n",
    "  add+=item\n",
    "df_cv=pd.DataFrame(add/len(l),index=train_set.columns,columns=[\"importance\"]).sort_values('importance', ascending=False)\n",
    "df_cv=df_cv.reset_index()\n",
    "feat_imp = pd.Series(df_cv.importance.values, index=df_cv.drop([\"importance\"], axis=1)).sort_values(axis='index',ascending=False)\n",
    "# \n",
    "feat_imp2=feat_imp[feat_imp>0.005]\n",
    "imp_columns=[]\n",
    "for item in pd.DataFrame(feat_imp2).reset_index()[\"index\"].tolist():\n",
    "  f=re.sub(\"[,]\",\"\",str(item))\n",
    "  try:  \n",
    "    columns= int(re.sub(\"['']\",\"\",f))\n",
    "    imp_columns.append(columns)\n",
    "  except:\n",
    "    columns= re.sub(\"['']\",\"\",f)\n",
    "  imp_columns.append(columns)\n",
    "# X_train_tree4=df_train_cat2[imp_columns]\n",
    "len(imp_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 609
    },
    "colab_type": "code",
    "id": "o6t7sMKhHMix",
    "outputId": "20528b6e-7313-456f-ca6f-d36c197e2a5c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f656e571780>"
      ]
     },
     "execution_count": 80,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_imp.nlargest(10).plot(kind='barh', figsize=(10,10))\n",
    "#as can be seen the engineered features are contributing heavily towards the overall model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "01NbamaAKDsT"
   },
   "outputs": [],
   "source": [
    "#submission module\n",
    "model=catboost_model2\n",
    "model.fit(TRAIN_FINAL.values,Y.values)\n",
    "pred=model.predict_proba(TEST_FINAL.values)\n",
    "pred_df=pd.DataFrame(pred)\n",
    "pred_df.rename(columns={0:1,1:2,2:3,3:4,4:5,5:6,6:7},inplace=True)\n",
    "pred_df['wilderness']=TEST_FINAL['wilderness']\n",
    "pred_df.rename(columns={1:'one',2:'two',3:'three',4:'four',5:'five',6:'six',7:'seven'},inplace=True)\n",
    "\n",
    "#the presence of wilderness and soil w.r.t to target in train \n",
    "\n",
    "# #tree wilderness map\n",
    "# wilderness 1=1,2,5,7\n",
    "# wilderness 2=1,5,7\n",
    "# wilderness 3= 1,2,3,5,6,7\n",
    "# wilderness 4=3,4,5,6\n",
    "\n",
    "#soil fam map\n",
    "#bross,#cryumbepts,#moran 1,5,7\n",
    "#cathedral,#pachic 3,4,6\n",
    "#como,#cryorthents 1,2,5,7\n",
    "#gothic 5\n",
    "#granile,#rogert 2,5,7\n",
    "#haploborolis 3,4,5\n",
    "#legault,#supervisor,#troutville 5,7\n",
    "#leighcan,#typic,#catamount 1,2,3,5,6,7\n",
    "#rock 1\n",
    "#vanet 2,3,4,5,6\n",
    "#bullwark,cryaquolis,#gateview 2,3,4,5,6,7\n",
    "\n",
    "#leveraging the similarity between test and train we can build a hypothesis that the cover types not present in the above combinations in train\n",
    "#will also not be present in similar combinations in test\n",
    "#as logloss punishes confidently wrong far more than it rewards confidently right, its imperative we try to decrease the first type of errors as below\n",
    "\n",
    "pred_df.loc[pred_df.wilderness == 4, ['seven', 'one','two']] = 0,0,0\n",
    "pred_df.loc[pred_df.wilderness == 3, ['four']] = 0\n",
    "pred_df.loc[pred_df.wilderness == 2, ['two','three','four','six']] = 0,0,0,0\n",
    "pred_df.loc[pred_df.wilderness == 1, ['three','four','six']] = 0,0,0\n",
    "pred_df.rename(columns={'one':1,'two':2,'three':3,'four':4,'five':5,'six':6,'seven':7},inplace=True)\n",
    "pred_df.drop(['wilderness'],axis=1,inplace=True)\n",
    "pred_df['soil_fam']=Test['soil_fam']\n",
    "pred_df.rename(columns={1:'one',2:'two',3:'three',4:'four',5:'five',6:'six',7:'seven'},inplace=True)\n",
    "pred_df.loc[pred_df.soil_fam == 'Bross', ['two', 'three','four','six']] = 0,0,0,0\n",
    "pred_df.loc[pred_df.soil_fam == 'Cryumbrepts', ['two', 'three','four','six']] = 0,0,0,0\n",
    "pred_df.loc[pred_df.soil_fam == 'Moran', ['two', 'three','four','six']] = 0,0,0,0\n",
    "pred_df.loc[pred_df.soil_fam == 'cathedral', ['one', 'two','five','seven']] = 0,0,0,0\n",
    "pred_df.loc[pred_df.soil_fam == 'Pachic', ['one', 'two','five','seven']] = 0,0,0,0\n",
    "pred_df.loc[pred_df.soil_fam == 'como', ['four','six']] = 0,0\n",
    "pred_df.loc[pred_df.soil_fam == 'Cryorthents', ['four','six']] = 0,0\n",
    "pred_df.loc[pred_df.soil_fam == 'Rogert', ['one','three', 'four','six']] = 0,0,0,0\n",
    "pred_df.loc[pred_df.soil_fam == 'Granile', ['one','three', 'four','six']] = 0,0,0,0\n",
    "pred_df.loc[pred_df.soil_fam == 'Haploborolis', ['one','two', 'seven']] = 0,0,0\n",
    "pred_df.loc[pred_df.soil_fam == 'Legault', ['one','two','three', 'four','six']] = 0,0,0,0,0\n",
    "pred_df.loc[pred_df.soil_fam == 'Supervisor', ['one','two','three', 'four','six']] = 0,0,0,0,0\n",
    "pred_df.loc[pred_df.soil_fam == 'Troutville', ['one','two','three', 'four','six']] = 0,0,0,0,0\n",
    "pred_df.loc[pred_df.soil_fam == 'Rock', ['two','three', 'four','five','six','seven']] = 0,0,0,0,0,0\n",
    "pred_df.loc[pred_df.soil_fam == 'vanet', ['one','seven']] = 0,0\n",
    "pred_df.loc[pred_df.soil_fam == 'Cryaquolis', ['one']] = 0\n",
    "pred_df.loc[pred_df.soil_fam == 'Bullwark', ['one']] = 0\n",
    "pred_df.loc[pred_df.soil_fam == 'Gateview', ['one']] = 0\n",
    "pred_df.loc[pred_df.soil_fam == 'Typic', ['four']] = 0\n",
    "pred_df.loc[pred_df.soil_fam == 'Leighcan', ['four']] = 0\n",
    "pred_df.loc[pred_df.soil_fam == 'Catamount', ['four']] = 0\n",
    "pred_df.loc[pred_df.soil_fam == 'Gothic', ['one','two','three', 'four','six','seven']] = 0,0,0,0,0,0\n",
    "pred_df.drop(['soil_fam'],axis=1,inplace=True)\n",
    "pred_df.rename(columns={'one':1,'two':2,'three':3,'four':4,'five':5,'six':6,'seven':7},inplace=True)\n",
    "\n",
    "pred_df=reduce_mem_usage(pred_df)\n",
    "\n",
    "pred_df.to_csv(\"/GD/My Drive/forest/sol1.csv\",index=False)\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "Forest_cover_prediction_Db.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
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 },
 "nbformat": 4,
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}
